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      TheCellMap.org: A Web-Accessible Database for Visualizing and Mining the Global Yeast Genetic Interaction Network

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          Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast Saccharomyces cerevisiae. In particular, TheCellMap.org allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize subnetworks, using data-driven network layouts in an intuitive and interactive manner.

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          Most cited references 18

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            The genetic landscape of a cell.

            A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
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              The mystery of missing heritability: Genetic interactions create phantom heritability.

              Human genetics has been haunted by the mystery of "missing heritability" of common traits. Although studies have discovered >1,200 variants associated with common diseases and traits, these variants typically appear to explain only a minority of the heritability. The proportion of heritability explained by a set of variants is the ratio of (i) the heritability due to these variants (numerator), estimated directly from their observed effects, to (ii) the total heritability (denominator), inferred indirectly from population data. The prevailing view has been that the explanation for missing heritability lies in the numerator--that is, in as-yet undiscovered variants. While many variants surely remain to be found, we show here that a substantial portion of missing heritability could arise from overestimation of the denominator, creating "phantom heritability." Specifically, (i) estimates of total heritability implicitly assume the trait involves no genetic interactions (epistasis) among loci; (ii) this assumption is not justified, because models with interactions are also consistent with observable data; and (iii) under such models, the total heritability may be much smaller and thus the proportion of heritability explained much larger. For example, 80% of the currently missing heritability for Crohn's disease could be due to genetic interactions, if the disease involves interaction among three pathways. In short, missing heritability need not directly correspond to missing variants, because current estimates of total heritability may be significantly inflated by genetic interactions. Finally, we describe a method for estimating heritability from isolated populations that is not inflated by genetic interactions.

                Author and article information

                G3 (Bethesda)
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                20 March 2017
                May 2017
                : 7
                : 5
                : 1539-1549
                [* ]The Donnelly Centre, University of Toronto, Ontario M5S 3E1, Canada
                []Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455
                []Simons Center for Data Analysis, Simons Foundation, New York, New York 10010
                [§ ]Lewis-Sigler Institute for Integrative Genomics, Princeton University, New Jersey 08544
                [** ]Department of Molecular Genetics, University of Toronto, Ontario M5S 3E1
                Author notes
                [1 ]Corresponding authors: Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455. E-mail: cmyers@ 123456cs.umn.edu ; and The Donnelly Centre, University of Toronto, 160 College Street, Room 13212, Toronto, ON M5S 3E1, Canada. E-mail: michael.costanzo@ 123456utoronto.ca ; brenda.andrews@ 123456utoronto.ca ; and charlie.boone@ 123456utoronto.ca
                Copyright © 2017 Usaj et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 8, Tables: 0, Equations: 0, References: 25, Pages: 11


                synthetic genetic array sga, yeast genetics, genetic network, genetic interactions


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